Recommender Systems Complete Course Beginner to Advanced - Motivation for Recommender System: Generations of Recommender

The video tutorial discusses the evolution of recommender systems across three generations. The first generation includes knowledge-based, content-based, and collaborative filtering systems, as well as hybrid systems. The second generation introduces matrix factorization, web usage mining, and personality-based systems. The third generation focuses on deep learning, product-based systems, and advanced collaborative filtering techniques. The tutorial also highlights the significance of recommender systems in the context of artificial intelligence.

Which of the following is NOT a part of the first generation recommender systems?

• Content-based systems

• Collaborative filtering

• Knowledge-based systems

• Deep learning systems

What is a key feature of the second generation recommender systems?

• Hybrid systems

• Matrix factorization

• Product-based recommendations

• Use of deep learning

Which approach in the second generation involves analyzing user behavior on the web?

• Personality-based recommendation

• Matrix factorization

• Web usage mining

• Content-based filtering

What technology is primarily used in the third generation deep content-based recommendation systems?

• Collaborative filtering

• Web usage mining

• Matrix factorization

• Deep learning

Which of the following is a characteristic of third generation recommender systems?

• Knowledge-based systems

• Product-based recommendations

• Content-based filtering

• Matrix factorization